# streaming-data-processing Published articles for streaming-data-processing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Rerouting the Stream: How Lyft Moved to the Apache Flink Operator DevFeed: [Rerouting the Stream: How Lyft Moved to the Apache Flink Operator]() Original publisher: [Read original article]() Author: Maheep Myneni Published: 2026-08-31T19:08:16Z Content type: article Language: en Sources: [Lyft Engineering - Medium]() Topics: [Apache Iceberg](), [Streaming](), [Kubernetes](), [Open Source](), [autoscaling](), [Tooling](), [Architecture & Design]() Tags: [apache](), [apache-flink](), [autoscaling](), [data-platforms](), [infrastructure](), [kubernetes](), [lyft](), [open-source](), [production](), [streaming](), [streaming-data-processing](), [tooling]() ### AI overview Lyft describes its migration from an internally developed Flink Kubernetes operator to the open-source Apache Flink Kubernetes Operator. The move addressed maintenance burden, technical debt, feature gaps, outdated dependencies, and growing real-time streaming demands, while providing capabilities such as autoscaling, memory tuning, safer upgrades, and automatic rollbacks. ### Source excerpt Written by Maheep Myneni, Arda Kuyumcu, and Prem Santosh Udaya Shankar at Lyft. Why We Migrated: Technical Debt Meets Modern Streaming Demands Over the past several quarters, Lyft's Streaming Compute team retired our internally developed Flink Kubernetes operator and moved our entire streaming fleet onto the open-source Apache Flink Kubernetes operator. This post is about why we made the switch, how we pulled it off incrementally without disrupting users, and the follow-on work it took to actually get the benefits we were after. Back in 2020, when we first architected the Lyft Flink Kubernetes Operator, it was exactly what we needed. At that time, the open-source community hadn't yet built a dedicated control plane, so we built our own to manage all streaming applications on Kubernetes. It worked well for our initial workloads, but as our real-time data needs increased and our engineers' scope of ownership grew in both breadth and complexity, the cracks started to show. First came the maintenance burden. Our operator had become a relic of Lyft's early Kubernetes days, kept alive by a growing pile of custom code. Every Flink version upgrade meant carefully picking through layers of accumulated technical debt and hoping nothing broke on the way through. Second came the feature gap. Streaming tooling kept moving, and our engineers kept asking for capabilities that had become table stakes elsewhere, such as autoscaling to right-size jobs, automatic rollbacks on failed deploys, and an end to hand-tuning CPU and memory. Each request left us with two options, neither of which was ideal. We could explain why we couldn't support it yet, or spend weeks rebuilding something the open-source community had already shipped. Third was the dependency problem. We were pinned to outdated libraries. That doesn't break anything today, but it almost always creates new issues down the line. Security patches lagged, modern Kubernetes features stayed out of reach, and every quarter we waite ## New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot DevFeed: [New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot]() Original publisher: [Read original article]() Author: Confluent Staff Published: 2026-08-18T14:00:10Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Artificial Intelligence](), [apache-flink](), [Kafka](), [streaming-data-processing](), [real-time](), [MCP Server](), [Agent Skills](), [MCP](), [Inference](), [Google](), [ibm]() Tags: [ai](), [ai-ready-data](), [ai-tools](), [apache-flink](), [confluent](), [confluent-cloud](), [google](), [ibm](), [kafka](), [mcp](), [mcp-server](), [real-time](), [skills](), [streaming-data-processing](), [support](), [tools]() ### AI overview Confluent announces updates to Confluent Intelligence and related AI tools for building production AI systems on Apache Kafka and Apache Flink. The release adds expanded time-series model support, a generally available Real-Time Context Engine, updates to the fully managed MCP Server, Agent Skills for AI coding assistants, and Confluent Copilot. The features are intended to provide agents and applications with fresh business context and governed access to live data and infrastructure. ### Source excerpt Explore new AI features and AI tools: support for IBM Granite Time Series models and TimesFM models (EA), enhanced Real-Time Context Engine experience, new Agent Skills, Confluent Copilot ## Why Flink may be unnecessarily complex for most streaming data processing users DevFeed: [Why Flink may be unnecessarily complex for most streaming data processing users]() Original publisher: [Read original article]() Author: Javi Santana Published: 2025-10-21T00:00:00Z Content type: opinion Language: en Sources: [Tinybird]() Topics: [streaming-data-processing](), [Streaming]() Tags: [flink](), [scalable-analytics-architecture](), [streaming](), [streaming-data-processing]() ### AI overview The article argues that Flink's complexity may make it unnecessary for most people who need streaming data processing. ### Source excerpt Flink might sound like the holy grail of streaming data processing, but for 95% of us, it's just a complex headache we don't need. ## Snyk Helps Secure the Golang Bento Project DevFeed: [Snyk Helps Secure the Golang Bento Project]() Original publisher: [Read original article]() Author: Phill Garrett Published: 2025-03-12T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [open-source-security](), [streaming-data-processing](), [Kafka](), [CI/CD]() Tags: [blog](), [ci-cd](), [container-security](), [developer](), [engineering](), [go](), [golang](), [interest](), [kafka](), [maintainers](), [open-source](), [open-source-security](), [pull-request](), [releases](), [security](), [snyk-open-source](), [streaming-data-processing](), [vulnerability]() ### AI overview Snyk describes contributing fixes to the open-source Go project Bento after finding a denial-of-service vulnerability in its SSH dependency. The post also outlines Bento's streaming-data role and Snyk's program for open-source maintainers. ### Source excerpt Discover how Snyk helps secure the open source Golang project Bento by contributing vulnerability fixes and leveraging AI-powered tools. Learn about our efforts to enhance Bento's security and support open source maintainers through the Snyk Secure Developer Program. ## Apache Beam for Search: Getting Started by Hacking Time DevFeed: [Apache Beam for Search: Getting Started by Hacking Time]() Original publisher: [Read original article]() Author: Doug Turnbull Published: 2021-01-08T15:00:01Z Content type: tutorial Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [streaming-data-processing](), [data-processing](), [AI Bots]() Tags: [apache](), [apis](), [batch](), [data-processing](), [event](), [events](), [real-time](), [search](), [shopify](), [spark](), [statistics](), [storage](), [stream-processing](), [streaming-data-processing](), [workflows]() ### AI overview An introduction to using Apache Beam for search-related streaming data processing. It explains how unified batch and streaming workflows can process clickstream data for real-time relevance tuning, and introduces event time, delayed events, and out-of-order data as core challenges. ### Source excerpt To create relevant search, processing clickstream data is key: you frequently want to promote search results that are being clicked on and purchased, and demote those things users don't love. Typically search systems think of processing clickstream data as a batch job run over historical data, perhaps using a system like Spark.